| Autor: |
Kumar, J. Naresh, Sheeba, Merlin, Swathi, M. |
| Předmět: |
|
| Zdroj: |
AIP Conference Proceedings; 2022, Vol. 2519 Issue 1, p1-7, 7p |
| Abstrakt: |
Nowadays, machine learning is playing vital role to extract and identify the useful features that best represent data in various fields, containing biological data analysis, content mining, and social investigations. Conventional clustering and feature selection techniques consider the information lattice as static. However, the information lattices advance easily after some time in numerous applications. The unsupervised pattern mining, existing co-clustering techniques always imagine that the information lattices are static; that is, they do not develop after some time. However, numerous real-world domains, the procedures that produced the information are time developing.The analytical study focuses on an evolutionary co-clustering formulation for recognizing co-clusters from time-changing information which employs sparsity-inducing regularization to recognize block structures from the time-changing information lattices in article.The analytical study reviews and studies the various predictions and clustering methodologies of machine learning with feature and limitations to design and efficient and robust technique to solve recent issues. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
|
